Bayesian Modelling of Time Series of Counts with Missing Data

• Type: Article in International Conference Proceedings Book
• Date: 2025

Authors

Silva, Isabel (Author) FEUP; Silva, Maria Eduarda (Author) FEP; Pereira, I (Author) Other

Conference proceedings International

Title: Springer Proceedings in Mathematics and Statistics

Pages: 83-98

26th Congress of the Portuguese Statistical Society, SPE 2023

Evora, 13 October 2021 through 16 October 2021

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Scopus - 0 Citations

Other information

Authenticus ID: P-017-T0W

DOI: 10.1007/978-3-031-68949-9_7

Abstract (EN): The presence of missing data poses a common challenge for time series analysis in general since the most usual requirement is that the data is equally spaced in time and therefore imputation methods are required. For time series of counts, the usual imputation methods which usually produce real valued observations, are not adequate. This work employs Bayesian principles for handling missing data within time series of counts, based on first-order integer-valued autoregressive (INAR) models, namely Approximate Bayesian Computation (ABC) and Gibbs sampler with Data Augmentation (GDA) algorithms. The methodologies are illustrated with synthetic and real data and the results indicate that the estimates are consistent and present less bias when the percentage of missing observations decreases, as expected. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15